Geometry-aware Two-scale PIFu Representation for Human Reconstruction
Authors: Zheng Dong, Ke Xu, Ziheng Duan, Hujun Bao, Weiwei Xu, Rynson Lau
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of our approach in reconstructing facial details and bodies of different poses and its superiority over state-of-the-art methods. |
| Researcher Affiliation | Academia | Zheng Dong1 Ke Xu2 Ziheng Duan1 Hujun Bao1 Weiwei Xu 1 Rynson W.H. Lau2 1State Key Lab of CAD&CG, Zhejiang University 2 City University of Hong Kong |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | We use the THuman2.0 [100] dataset which contains 500 high-quality 3D human scans to train and validate our network. We split the dataset into training and test sets with a ratio of 4:1. For PIFu-Face, we pretrain Ff using the Face Scape [94] dataset, which contains 3D head models of different people and expressions. |
| Dataset Splits | No | We use the THuman2.0 [100] dataset which contains 500 high-quality 3D human scans to train and validate our network. We split the dataset into training and test sets with a ratio of 4:1. |
| Hardware Specification | No | This information is provided in the supplemental material. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | We explain the implementation details in the supplemental material. |